A Proposed Ethical Framework on AI Mitigation Strategies in Decision Support Systems: Case Study
Author : Dr. Aparna Vaidyanathan, Dr. Kavita Khobragade and Dr. Deepali Dhainje
Abstract :
Artificial Intelligence (AI) systems have rapidly integrated into critical areas such as healthcare, finance, law enforcement, and education, offering powerful decision making capabilities. As decision making involves factors and emotions, the association rules, references that are applied play an essential role in decision making. Today artificial intelligence raises expectations for faster, more accurate, more rational and fairer decisions with technological advancements. But if these systems behave with their predictions differently than with their parameters. A framework can optimize and enhance the outcome efficiently.
As the primary purpose of this research paper is to examine the bias in the decision-making process of AI systems, the paper focuses on proposing a framework that can optimize the ambiguity that is defined as a systematic error in decision making processes that results in unfair outcomes. In the context of AI, ambiguity can arise from various sources, including data collection, algorithm design, machine learning models. The system can learn and replicate patterns of ambiguity that is present in the data used to train them, resulting in unfair or discriminatory outcomes. It is important to identify and address ambiguity in AI to ensure that these systems are fair and equitable for all users.
This paper proposes a methodology framework, a recommendation for AI systems in medical diagnosis that can assist and behave to the nearest accuracy.
Keywords :
Artificial intelligence, ethical decision making, mitigation, framework, medical diagnosis, algorithm, optimization.
